AlertGS: determining alerts for gene sets.

Abstract

Motivation: A typical goal in gene expression studies is identifying certain gene sets enriched with significant genes. The measurement of many gene expression experiments for several concentrations or time points allows the modeling of the concentration/time-response relationship for each gene, and the subsequent estimation of a gene-wise alert. In this work, an approach is proposed to transfer the concept of alerts from single genes to gene sets, yielding a global significance statement and the respective concentration or time where the first enrichment of the gene set can be observed. The methodology is based on a Kolmogorov-Smirnoff type test statistic for each gene set.

Results: Simulations show that a majority of these sets can be identified especially for lower numbers of true gene sets with a signal. The false positive rate can be controlled by subsequent decorrelation approaches. Overall, the true gene set-wise alerts are rarely overestimated and rather tend to be underestimated.

Availability and implementation: The code needed to reproduce the simulations and apply the AlertGS methodology is available at the GitHub repository: https://github.com/FKappenberg/AlertGS.